How is real-time data sync achieved?

Real-time data sync is achieved by combining event-driven architectures, efficient communication protocols, and conflict resolution strategies. The core idea is to detect data changes instantly and propagate them to all connected systems. This typically involves monitoring data sources for updates, transmitting those changes through low-latency channels, and ensuring all clients receive consistent state updates.

The first step involves detecting changes at the source. Databases often use triggers, change data capture (CDC) tools, or transaction log monitoring to identify modifications. For example, PostgreSQL’s logical decoding or tools like Debezium can stream database changes as events. These events are then published to a message broker (e.g., Apache Kafka or RabbitMQ) using a publish-subscribe pattern. Clients subscribe to relevant topics and receive updates as soon as they occur. In mobile/web apps, client-side SDKs like Firebase Realtime Database or Apollo GraphQL subscriptions track local changes and push them to a central server.

The actual data transmission relies on protocols optimized for real-time communication. WebSocket connections maintain persistent, bidirectional channels between servers and clients, allowing instant push notifications instead of repeated polling. For instance, a collaborative editing tool like Google Docs uses Operational Transformations sent over WebSockets to synchronize cursor positions and text changes across users. When conflicts arise (e.g., two users editing the same field), strategies like last-write-wins, version vectors, or application-specific merge logic resolve discrepancies. Services like CRDTs (Conflict-Free Replicated Data Types) automate this by designing data structures that guarantee consistency across distributed updates.

Finally, caching and state management ensure efficiency. In-memory databases like Redis cache frequently accessed data and use pub/sub mechanisms to broadcast updates. Edge networks like Cloudflare Workers can reduce latency by serving cached data geographically closer to users. A stock trading platform might combine Redis for real-time price updates with WebSocket connections to brokers, while using version stamps to ensure traders always see the latest bid/ask data. Authentication and authorization checks are typically handled at the gateway level (e.g., using JWTs) to maintain security without blocking the data stream.